Technological developments, such as mobile augmented reality (MAR) and Internet of Things (IoT) devices, have expanded available data and interaction modalities for mobile applications. This development enables intuitive data presentation and provides real-time insights into the user’s context. Due to the proliferation of available IoT data sources, user interfaces (UIs) have become complex and diversified, while mobile devices have limited screen spaces. This state increases the necessity of design principles that help to secure sufficient user experience (UX). We found that studies of design principles for IoT-enabled MAR applications are limited. Therefore, we conducted a systematic literature review to identify existing design principles applicable to IoT-enabled MAR applications. From the state-of-the-art research, we compiled and categorized 26 existing design principles into seven categories. We analyzed the UIs of three IoT-enabled MAR applications with the identified design principles and user feedback gathered from each application’s evaluation to understand what design principles can be considered in designing these applications. Among the 26 principles, we find eight principles that are commonly identified as possible improvements for the applications based on their purposes. We demonstrate the practical use of the identified principles by redesigning the UIs, and we propose five new design principles derived from the application analysis. As a result, we summarized a total of 31 design principles, including the five new ones. We expect that our findings will give insight into the UX/UI design of IoT-enabled MAR applications for researchers, educators, and practitioners interested in UX/UI development.

The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

Real-time anomaly detection in energy consumption is crucial for identifying technical inefficiencies and user behavior issues that lead to energy waste. Traditional methods rely on utilizing large historical consumption patterns, but data limitations in certain domains hinder the application of these systems. This study addresses this challenge by leveraging transfer learning with long short-term memory networks, using school building energy datasets as a source domain to improve performance in data-scarce target domains. The evaluated models were trained to forecast 8-h energy consumption and detect anomalies. Results show that transfer learning models trained with 40 % of the dataset generalize better, reducing sensitivity to minor fluctuations and lowering false alarm rates compared to baseline models which trained on full training dataset. Those models tend to overfit to small variations, which led to increased false positives. These findings highlight the transfer learning effectiveness in improving anomaly detection reliability, ensuring models focus on consistent and persistent changes in consumption patterns.

Interactivity is a metric that measures the level of control or manipulation users may exert over a system, software, or service. It is considered a key dimension in cloud-gaming services, measuring how effectively users can control and respond to game events in real-time. Although widely acknowledged by standards such as ITU-T Rec. G.1051, G.1072, its relationship to other quality dimensions such as video, audio, and overall Quality of Experience (QoE), remains not thoroughly examined. In this paper, we present a novel Bayesian Network-based framework to model and analyze interactivity alongside other quality factors under varied network conditions. Our method enables probabilistic inference and sensitivity analysis across perceptual variables, offering explainable insights into their mutual influence. Using data from two (\textit{N1}=30, and \textit{N2}=31 subjects) subjective studies — one for Virtual Reality Cloud Gaming (VRCG) and another for Mobile Cloud Gaming (MCG) — we show interactivity is most sensitive to round trip time (RTT) but resilient to jitter (RJ) effect. Further, an assessment of the seven interactivity-only variables shows that their distributions change uniformly under varying network conditions, suggesting that interactivity may be captured by a single metric. Finally, sensitivity analyses indicate that QoE is a more representative metric than interactivity for quality assessment in cloud gaming over heterogeneous access networks.

In the future, extended reality (XR) applications will be hosted on cloud and edge infrastructures and streamed over heterogeneous access networks such as Wi-Fi and 6G. These infrastructures promise ubiquity but are prone to stochastic conditions, such as network congestion and wireless signal fading and attenuation, that can be detrimental to the quality of experience (QoE) regarding XR applications. This paper presentsextensive and novel results assessing the impact of networkconditions (N = 20) on users’ QoE via realistic subjective tests(N = 28) involving factors such as round-trip time (RTT), jitter(RJ), and packet loss (PL) in a social XR application context. Our results indicate that social XR applications require stringent quality of service to support users’ QoE. We demonstrate that RTT values up to 77 ms do not significantly impact users’ QoE. Furthermore, combined (PL and RTT) values lead to significant QoE degradation when RTT values exceed 77 ms and PL exceeds 2%. We also demonstrate that even minimal jitter values, with 1 standard deviation beyond 52 ms RTT values,can lead to significant QoE degradations. Furthermore, jitter values exceeding 3 standard deviations for 27ms RTT value and beyond should be avoided. Finally, based on network traffic data between Sweden and various AWS data centers in Europe, we show that social XR applications can be hosted at a few datacenter locations with minimal QoE impact for wired network connections. However, due to high jitter values, both 4G and 5G networks are not conducive to users’ QoE.